




'''simple docstring'''


import numpy as np
import torch
from torch.utils.data import Dataset

from utils import logger





class  __lowercase    (	__magic_name__			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   )					->     List[Any]:
     __a								=       params

     __a								=       np.array(UpperCamelCase   )
     __a								=       np.array([len(UpperCamelCase   ) for t in data]   )

     self.check()
     self.remove_long_sequences()
     self.remove_empty_sequences()
     self.remove_unknown_sequences()
     self.check()
     self.print_statistics()







   def __getitem__(						self   ,				UpperCamelCase   )					->     List[str]:
     return (self.token_ids[index], self.lengths[index])







   def __len__(						self   )					->     Optional[Any]:
     return len(self.lengths   )







   def 		UpperCamelCase__      (						self   )					->     str:
     assert len(self.token_ids   ) == len(self.lengths   )
     assert all(self.lengths[i] == len(self.token_ids[i]   ) for i in range(len(self.lengths   )   )   )







   def 		UpperCamelCase__      (						self   )					->     Union[str, Any]:
     __a								=       self.params.max_model_input_size
     __a								=       self.lengths > max_len
     logger.info(f"Splitting {sum(UpperCamelCase   )} too long sequences."   )

     def divide_chunks(UpperCamelCase   ,				UpperCamelCase   ):
       return [l[i : i + n] for i in range(0   ,				len(UpperCamelCase   )   ,				UpperCamelCase   )]

     __a								=       []
     __a								=       []
     if self.params.mlm:
       __a						,							__a								=       self.params.special_tok_ids['cls_token'], self.params.special_tok_ids['sep_token']
     else:
       __a						,							__a								=       self.params.special_tok_ids['bos_token'], self.params.special_tok_ids['eos_token']

     for seq_, len_ in zip(self.token_ids   ,				self.lengths   ):
       assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
       if len_ <= max_len:
         new_tok_ids.append(seq_   )
         new_lengths.append(len_   )
       else:
         __a								=       []
         for sub_s in divide_chunks(seq_   ,				max_len - 2   ):
           if sub_s[0] != cls_id:
             __a								=       np.insert(UpperCamelCase   ,				0   ,				UpperCamelCase   )
           if sub_s[-1] != sep_id:
             __a								=       np.insert(UpperCamelCase   ,				len(UpperCamelCase   )   ,				UpperCamelCase   )
           assert len(UpperCamelCase   ) <= max_len
           assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
           sub_seqs.append(UpperCamelCase   )

         new_tok_ids.extend(UpperCamelCase   )
         new_lengths.extend([len(UpperCamelCase   ) for l in sub_seqs]   )

     __a								=       np.array(UpperCamelCase   )
     __a								=       np.array(UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     Tuple:
     __a								=       len(self   )
     __a								=       self.lengths > 11
     __a								=       self.token_ids[indices]
     __a								=       self.lengths[indices]
     __a								=       len(self   )
     logger.info(f"Remove {init_size - new_size} too short (<=11 tokens) sequences."   )







   def 		UpperCamelCase__      (						self   )					->     str:
     if "unk_token" not in self.params.special_tok_ids:
       return
     else:
       __a								=       self.params.special_tok_ids['unk_token']
     __a								=       len(self   )
     __a								=       np.array([np.count_nonzero(a == unk_token_id   ) for a in self.token_ids]   )
     __a								=       (unk_occs / self.lengths) < 0.5
     __a								=       self.token_ids[indices]
     __a								=       self.lengths[indices]
     __a								=       len(self   )
     logger.info(f"Remove {init_size - new_size} sequences with a high level of unknown tokens (50%)."   )







   def 		UpperCamelCase__      (						self   )					->     Dict:
     if not self.params.is_master:
       return
     logger.info(f"{len(self   )} sequences"   )
     # data_len = sum(self.lengths)
     # nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
     # logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')

     # unk_idx = self.params.special_tok_ids['unk_token']
     # nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
     # logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       [t[0] for t in batch]
     __a								=       [t[1] for t in batch]
     assert len(UpperCamelCase   ) == len(UpperCamelCase   )

     # Max for paddings
     __a								=       max(UpperCamelCase   )

     # Pad token ids
     if self.params.mlm:
       __a								=       self.params.special_tok_ids['pad_token']
     else:
       __a								=       self.params.special_tok_ids['unk_token']
     __a								=       [list(t.astype(UpperCamelCase   )   ) + [pad_idx] * (max_seq_len_ - len(UpperCamelCase   )) for t in token_ids]
     assert len(tk_   ) == len(UpperCamelCase   )
     assert all(len(UpperCamelCase   ) == max_seq_len_ for t in tk_   )

     __a								=       torch.tensor(tk_   )  # (bs, max_seq_len_)
     __a								=       torch.tensor(UpperCamelCase   )  # (bs)
     return tk_t, lg_t









'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



